This invention relates to methods for detecting signal features from a biological (bio) signal in the presence of noise.
An electrocardiogram (ECG or EKG), is a graphic produced by an electrocardiograph, which records the electrical activity (a signal) of the heart over time. Electrical waves cause the heart muscle to pump. These waves pass through the body and can be measured at electrodes (electrical contacts) attached to the skin. Electrodes on different sides of the heart measure the activity of different parts of the heart muscle. An EKG displays the voltage (a signal) between pairs of these electrodes, and the muscle activity that they measure, from different directions. This display indicates the overall rhythm of the heart, and weaknesses in different parts of the heart muscle. It is a way to measure and diagnose abnormal rhythms of the heart, particularly abnormal rhythms caused by damage to the conductive tissue that carries electrical signals, or abnormal rhythms caused by levels of salts, such as potassium, that are too high or low.
The ability to analyze an EKG signal and detect variances therein, provides the ability to monitor the physiological condition of a heart. For instance, accurate detection of variances in an EKG signal allows for the detection of heart events, such as heartbeat detection, arrhythmias, ischemias, and a myriad of other events. To detect variances in the EKG signal, it is necessary to minimize or eliminate noise, which also causes variances in an EKG signal, but does not correspond to a physiological event of the heart. Otherwise, the noise variance may be misconstrued as a heart event. In turn, this can lead to a potential misdiagnosis, false positive, missed event, or failure to detect other rhythms; among other undesirable results. In particular, noise can lead to false identification of R waves, resulting in the detection of more heartbeats than what actually occurred. Misidentification may occur when, for example, baseline drift is present in the signal.
One known method of removing low frequency noise involves using a low pass filter followed by a length transform, which computes the length of a curve within a given time window. When the length exceeds a predetermined threshold, the curve is identified as being an R wave. When baseline drift is present, portions of the curve that should be flat are instead sloped. This may increase the calculated curve length and lead to misidentification of the curve as an R wave.
In addition, baseline drift may add noise to the length transform, reducing the transform's definition and therefore causing a failure to perceive smaller peaks, which results in underreporting of R waves. Accordingly, a need exists for techniques which are able to detect heartbeats, along with signal features of other bio signals (such as brain waves), in the presence of noise.